Learning dynamics

Whenever we work, study, play, we constantly move from one piece of information to another. In so doing, we explore what could be defined an information or a knowledge space. Points of this space are components of our knowledge and culture, linked through semantic and logic relations. Whatever the activity we are involved in, while wandering on such networked structures, we describe paths, possibly expanding the space itself and creating novel connections. Still, how the conceptual spaces are structured, how we stand in them and how we shape our trajectories on them are all largely unknown.

Network theory and complex systems analysis can help address these questions, by providing us with a rigorous framework to investigate and model our dynamics as learners and information seekers. By basing on previous findings in cognitive and linguistic research as well as on the actual behaviour of learners in knowledge networks like Wikipedia, we aim at grasping general patterns in learning dynamics. This understanding is indeed key to design novel strategies and tools to make our experiences as knowledge explorers more and more efficient.

Related posts

New paper on learning dynamicsA new paper about "Optimal learning paths in information networks" just appeared on SciRep. A novel approach to algorithmic education is here proposed, which combines
NetSci2015 - Talk on learning dynamicsGiovanna Chiara Rodi will give a contributed talk during the NetSci2015 Conference on learning dynamics on networks. Abstract Optimal learning paths in information networks Each

@article{Rodi2015,
title = {Optimal Learning Paths in Information Networks},
author = {Giovanna Chiara Rodi and Vittorio Loreto and Vito DP Servedio and Francesca Tria},
url = {http://www.nature.com/srep/2015/150601/srep10286/full/srep10286.html},
doi = {10.1038/srep10286},
year = {2015},
date = {2015-06-01},
journal = {Scientific Reports},
volume = {5},
number = {10286},
abstract = {Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.

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